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Most of the existing virtual scenarios built for the digital protection of Chinese classical private gardens are too modern in expression style to show the aesthetic significance of their historical period. Considering the aesthetic commonality between traditional Chinese landscape paintings and classical private gardens and referring to image style transfer, here, a deep neural network was proposed to transfer the aesthetic style from landscape paintings to the virtual scenario of classical private gardens. The network consisted of two parts: style prediction and style transfer. The style prediction network was used to obtain style representation from style paintings, and the style transfer network was used to transfer style representation to the content scenario. The pre-trained network was then embedded into the scenario rendering pipeline and combined with the screen post-processing method to realise the stylised expression of the virtual scenario. To verify the feasibility of this methodology, a virtual scenario of the Humble Administrator’s Garden was used as the content scenario and five garden landscape paintings from different time periods and painting styles were selected for the case study. The results demonstrated that this methodology could effectively achieve the aesthetic style transfer of a virtual scenario.

Most of the existing virtual scenarios built for the digital protection of Chinese classical private gardens are too modern in expression style to show the aesthetic significance of their historical period. Considering the aesthetic commonality between traditional Chinese landscape paintings and classical private gardens and referring to image style transfer, here, a deep neural network was proposed to transfer the aesthetic style from landscape paintings to the virtual scenario of classical private gardens. The network consisted of two parts: style prediction and style transfer. The style prediction network was used to obtain style representation from style paintings, and the style transfer network was used to transfer style representation to the content scenario. The pre-trained network was then embedded into the scenario rendering pipeline and combined with the screen post-processing method to realise the stylised expression of the virtual scenario. To verify the feasibility of this methodology, a virtual scenario of the Humble Administrator's Garden was used as the content scenario and five garden landscape paintings from different time periods and painting styles were selected for the case study. The results demonstrated that this methodology could effectively achieve the aesthetic style transfer of a virtual scenario. [ABSTRACT FROM AUTHOR]

As an instance of semiotic interpretation of political art, this article rereads a painting created during the 1950s by Shi Lu that depicts the Chinese Communist leader, Mao Zedong. The author identifies the artist's visual references to traditional Chinese landscape painting and the embodied traditional values, differentiates the work from the popular revolutionary art style of the same age, and argues that this act of referencing problematizes the dominant ideology in a politically highly charged historical con text by reconstructing the commonly depicted political icon Mao through dislocated style and scale. The interpretation demonstrates how signifiers both in the forms of text and memory can interfere with current cultural drive and rename the signified through subtle variations. [ABSTRACT FROM AUTHOR]